Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System

Moore, Jasmine A.; Tuladhar, Anup; Ismail, Zahinoor; Mouchès, Pauline; Wilms, Matthias; Forkert, Nils D.

doi:10.1007/s12021-022-09602-6

Neuroinform

2022

DOI: 10.1007/s12021-022-09602-6

|View full text |Cite

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System

Jasmine A. Moore

Anup Tuladhar

Zahinoor Ismail

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article3

Relationship

Self Cite1

Independent2

Authors

Journals

Cited by 3 publications

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Nehmeh,

Rebehmed,

Nehmeh

et al. 2024

Drug Discovery Today

View full text Add to dashboard Cite

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Nehmeh,

Rebehmed,

Nehmeh

et al. 2024

Drug Discovery Today

View full text Add to dashboard Cite

Visual Functions Are Associated with Biomarker Changes in Alzheimer’s Disease

Memon,

Moore,

Kang

et al. 2024

JAD

View full text Add to dashboard Cite

Background: While various biomarkers of Alzheimer’s disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain. Objective: To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum. Methods: Data from participants (N = 1,460) in the Alzheimer’s Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations. Results: MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aβ1–42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers. Conclusions: Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.

show abstract

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system

Moore,

Wilms,

Gutierrez

et al. 2023

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model’s object recognition baseline performance. The results showed with retraining, model object recognition abilities are subject to a smoother and more gradual decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with Alzheimer’s disease (AD). Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model when compared to the injured model without retraining. Furthermore, we conducted this analysis on a network that had been extensively pruned, resulting in an optimized number of parameters or synapses. Our findings show that this network exhibited remarkably similar capability to recover task performance with decreasingly viable pathways through the network. In conclusion, adding a retraining step to the in-silico setup that simulates neuroplasticity improves the model’s biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System

Cited by 3 publications

References 42 publications

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Visual Functions Are Associated with Biomarker Changes in Alzheimer’s Disease

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system

Contact Info

Product

Resources

About